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Research Article

Effects of guiding vanes and orifice jet flow of a metered-dose inhaler on drug dosimetry in human respiratory tract

Xiuhua A. Si1Mohamed Talaat2Jinxiang Xi2( )
Department of Aerospace, Industrial, and Mechanical Engineering, California Baptist University, 8432 Magnolia Ave, Riverside, CA 92504, USA
Department of Biomedical Engineering, University of Massachusetts, 1 University Ave., Lowell, MA 01854, USA
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Abstract

Accurate modeling and simulation of metered-dose inhaler (MDI) drug delivery require detailed information about the spray aerosols and carrier airflows, which are sensitive to the geometry and formulation of the inhaler. This study aimed to systemically examine the effects of the MDI canister–holder guiding vanes and the orifice airflow on inhalation dosimetry. An MDI model was reconstructed from an actual inhaler that included a 0.5-mm-diameter orifice and six vertical guiding vanes on the inner wall of the canister–holder. Large-eddy simulation was used to capture the transient concurrent inspiratory and orifice airflows, and spray aerosols were tracked using the Lagrangian method. Measured aerosol size distribution and velocity were used to develop the computational model. Results show that MDI spray plume transport and deposition are sensitive to the instantaneous flow structures. Excluding the guiding vanes increased the mouth deposition by 8% (from 60% to 68%), while excluding the orifice jet flow decreased the mouth deposition by 5.5% (from 60% to 54.5%) compared to the control case. The impact of these two geometrical and flow details could persist in the small airways. The penetration rate to the left-lower lobe beyond the nineth generation (G9) increased by 67% when neglecting guiding vanes and increased by 50% when neglecting orifice flow.

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Experimental and Computational Multiphase Flow
Pages 247-261
Cite this article:
Si XA, Talaat M, Xi J. Effects of guiding vanes and orifice jet flow of a metered-dose inhaler on drug dosimetry in human respiratory tract. Experimental and Computational Multiphase Flow, 2023, 5(3): 247-261. https://doi.org/10.1007/s42757-022-0141-y

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Received: 20 April 2022
Revised: 16 June 2022
Accepted: 27 June 2022
Published: 10 January 2023
© Tsinghua University Press 2023
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